Seeds for t_plot_fit

Also:
* remove t_plot_fit from run_tests
* add short README for pytest
* add to setup.py

gpfit/tests/README

@@ -0,0 +1,10 @@
+## Plot unit tests
+
+Plot unit tests are run with [pytest-mpl](https://github.com/matplotlib/pytest-mpl)
+
+To generate the baseline images:
+`pytest --mpl-generate-path=baseline t_plot_fit.py`
+
+To test against the baseline images:
+`pytest --mpl t_plot_fit.py`
+

gpfit/tests/run_tests.py

@@ -18,9 +18,6 @@
 from gpfit.tests import t_print_fit
 TESTS += t_print_fit.TESTS
 
-from gpfit.tests import t_plot_fit
-TESTS += t_plot_fit.TESTS
-
 from gpfit.tests import t_examples
 TESTS += t_examples.TESTS
 

gpfit/tests/t_plot_fit.py

@@ -3,6 +3,8 @@
 import numpy as np
 from gpfit.fit import MaxAffine, SoftmaxAffine, ImplicitSoftmaxAffine
 
+SEED = 33404
+
 
 class TestPlot:
     "Unit tests for plot methods"
@@ -16,19 +18,19 @@ class TestPlot:
 
     @pytest.mark.mpl_image_compare(filename='ma_1d.png')
     def test_max_affine(self):
-        f = MaxAffine(self.x, self.y, self.K)
+        f = MaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot()
         return fig
 
     @pytest.mark.mpl_image_compare(filename='sma_1d.png')
     def test_softmax_affine(self):
-        f = SoftmaxAffine(self.x, self.y, self.K)
+        f = SoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot()
         return fig
 
     @pytest.mark.mpl_image_compare(filename='isma_1d.png')
     def test_implicit_softmax_affine(self):
-        f = ImplicitSoftmaxAffine(self.x, self.y, self.K)
+        f = ImplicitSoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot()
         return fig
 
@@ -47,19 +49,19 @@ class TestPlotSurface:
 
     @pytest.mark.mpl_image_compare(filename='ma_2d_surface.png')
     def test_max_affine(self):
-        f = MaxAffine(self.x, self.y, self.K)
+        f = MaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_surface(azim=135)
         return fig
 
     @pytest.mark.mpl_image_compare(filename='sma_2d_surface.png')
     def test_softmax_affine(self):
-        f = SoftmaxAffine(self.x, self.y, self.K)
+        f = SoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_surface(azim=135)
         return fig
 
     @pytest.mark.mpl_image_compare(filename='isma_2d_surface.png')
     def test_implicit_softmax_affine(self):
-        f = ImplicitSoftmaxAffine(self.x, self.y, self.K)
+        f = ImplicitSoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_surface(azim=135)
         return fig
 
@@ -79,19 +81,19 @@ class TestPlotSlices:
 
     @pytest.mark.mpl_image_compare(filename='ma_2d_slices.png')
     def test_max_affine(self):
-        f = MaxAffine(self.x, self.y, self.K)
+        f = MaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_slices()
         return fig
 
     @pytest.mark.mpl_image_compare(filename='sma_2d_slices.png')
     def test_softmax_affine(self):
-        f = SoftmaxAffine(self.x, self.y, self.K)
+        f = SoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_slices()
         return fig
 
     @pytest.mark.mpl_image_compare(filename='isma_2d_slices.png')
     def test_implicit_softmax_affine(self):
-        f = ImplicitSoftmaxAffine(self.x, self.y, self.K)
+        f = ImplicitSoftmaxAffine(self.x, self.y, self.K, seed=SEED)
         fig, _ = f.plot_slices()
         return fig
 

setup.py

@@ -30,7 +30,14 @@
     author_email="gpkit@mit.edu",
     url="https://github.com/convexengineering/gpfit",
     python_requires=">=3.4",
-    install_requires=["numpy", "scipy", "gpkit", "matplotlib"],
+    install_requires=[
+        "numpy",
+        "scipy",
+        "gpkit",
+        "matplotlib==3.3.2",
+        "pytest==6.2.5",
+        "pytest-mpl==0.13",
+    ],
     version="0.2.0",
     packages=["gpfit", "gpfit.maths", "gpfit.tests", "gpfit.xfoil"],
     include_package_data=True,